Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms

—Location check-ins contain both geographical and semantic information about the visited venues, in the form of tags (e.g., “restaurant”). Such data might reveal some personal information about users beyond what they actually want to disclose, hence their privacy is threatened. In this paper, we study users’ motivations behind location check-ins, and we quantify the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. By means of a targeted userstudy on Foursquare (N = 77), we show that the motivation behind Foursquare check-ins is a mediator of the loss of utility caused by generalization. Using these findings, we propose a machinelearning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for utility. Our results show that the model accurately predicts the loss of utility caused by semantic and geographical generalization; this model enables the design of utility-awar...

• Type: Journal
• Year: 2015
• Where: NDSS
• Authors: Igor Bilogrevic, Kévin Huguenin, Stefan Mihaila, Reza Shokri, Jean-Pierre Hubaux
• Keywords: Computer Networks, NDSS 2015